Two cloud masks for sentinel 2
1. Create a new script called cloud_module with the code below and save in your repository
var cloudScoreThresh = 20;
var cloudScorePctl = 0;
var contractPixels = 1.5;
var dilatePixels = 2.5;
////////////////////////////////////////////////////////////////////////////////
// Compute a cloud score and adds a band that represents the cloud mask.
// Cloud masking algorithm for Sentinel2. Built on ideas from Landsat cloudScore
// algorithm. Currently in beta and may need tweaking for individual study areas
exports.sentinelCloudScore = function(s2s) {
function getCloudScore(img){
// Compute several indicators of cloudyness and take the minimum of them.
var score = ee.Image(1);
var blueCirrusScore = ee.Image(0);
// Clouds are reasonably bright in the blue or cirrus bands.
//Use .max as a pseudo OR conditional
blueCirrusScore = blueCirrusScore.max(rescale(img, 'img.blue', [0.1, 0.5]));
blueCirrusScore = blueCirrusScore.max(rescale(img, 'img.cb', [0.1, 0.5]));
blueCirrusScore = blueCirrusScore.max(rescale(img, 'img.cirrus', [0.1, 0.3]));
score = score.min(blueCirrusScore);
// Clouds are reasonably bright in all visible bands.
score = score.min(rescale(img, 'img.red + img.green + img.blue', [0.2, 0.8]));
// Clouds are reasonably bright in all infrared bands.
score = score.min(rescale(img, 'img.nir + img.swir1 + img.swir2', [0.3, 0.8]));
// However, clouds are not snow.
var ndsi = img.normalizedDifference(['green', 'swir1']);
score = score.min(rescale(ndsi, 'img', [0.8, 0.6]));
score = score.multiply(100).byte();
score = score.clamp(0,100);
return img.addBands(score.rename(['cloudScore']));
}
function maskScore(img){
//var cloudMask = img.select(['cloudScore']).subtract(minCloudScore).lt(cloudScoreThresh)
// .focal_max(contractPixels).focal_min(dilatePixels).rename('cloudMask');
var cloudMask = img.select(['cloudScore']).lt(cloudScoreThresh).focal_max(contractPixels).focal_min(dilatePixels).rename('cloudMask');
return img.updateMask(cloudMask).addBands(cloudMask);
}
s2s = s2s.map(getCloudScore);
// Find low cloud score pctl for each pixel to avoid comission errors
var minCloudScore = s2s.select(['cloudScore']).reduce(ee.Reducer.percentile([cloudScorePctl]));
s2s = s2s.map(maskScore);
return s2s;
};
////////////////////////////////////////////////////////////////////////////////
// Function to mask clouds using the Sentinel-2 QA band.
exports.QAMaskCloud= function(collection) {
function maskClouds(image){
var qa = image.select('QA60').int16();
// Bits 10 and 11 are clouds and cirrus, respectively.
var cloudBitMask = Math.pow(2, 10);
var cirrusBitMask = Math.pow(2, 11);
// Both flags should be set to zero, indicating clear conditions.
var mask = qa.bitwiseAnd(cloudBitMask).eq(0).and(qa.bitwiseAnd(cirrusBitMask).eq(0));
// Return the masked and scaled data.
return image.updateMask(mask);
}
collection = collection.map(maskClouds);
return collection;
};
////////////////////////////////////////////////////////////////////////////////
// Helper function to apply an expression and linearly rescale the output.
// Used in the sentinelCloudScore function below.
function rescale(img, exp, thresholds) {
return img.expression(exp, {img: img})
.subtract(thresholds[0]).divide(thresholds[1] - thresholds[0]);
}
link
2. add the code below to your main script:
var clouds = require("users/user/landcoverS2:cloud_module");
print("applying QA cloud mask");
s2 = clouds.QAMaskCloud(s2);
Map.addLayer(ee.Image(s2.first()),{min:0,max:0.3,bands:"swir2,nir,red"},"after QA cloudmask");
print(ee.Image(s2.first()));
print("applying sentinel cloud mask");
s2 = clouds.sentinelCloudScore(s2);
Map.addLayer(ee.Image(s2.first()),{min:0,max:0.3,bands:"swir2,nir,red"},"after s2 cloudmask");
print(ee.Image(s2.first()));